Overview

Dataset statistics

Number of variables53
Number of observations39717
Missing cells4516
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory16.1 MiB
Average record size in memory424.0 B

Variable types

NUM24
CAT22
BOOL6
URL1

Reproduction

Analysis started2020-08-28 06:00:06.651847
Analysis finished2020-08-28 06:01:31.180300
Duration1 minute and 24.53 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

pymnt_plan has constant value "n" Constant
initial_list_status has constant value "f" Constant
policy_code has constant value "1" Constant
application_type has constant value "INDIVIDUAL" Constant
acc_now_delinq has constant value "0" Constant
delinq_amnt has constant value "0" Constant
int_rate has a high cardinality: 371 distinct values High cardinality
emp_title has a high cardinality: 28820 distinct values High cardinality
issue_d has a high cardinality: 55 distinct values High cardinality
title has a high cardinality: 19615 distinct values High cardinality
zip_code has a high cardinality: 823 distinct values High cardinality
earliest_cr_line has a high cardinality: 526 distinct values High cardinality
revol_util has a high cardinality: 1089 distinct values High cardinality
last_pymnt_d has a high cardinality: 101 distinct values High cardinality
last_credit_pull_d has a high cardinality: 106 distinct values High cardinality
member_id is highly correlated with idHigh correlation
id is highly correlated with member_idHigh correlation
funded_amnt is highly correlated with loan_amnt and 3 other fieldsHigh correlation
loan_amnt is highly correlated with funded_amnt and 2 other fieldsHigh correlation
funded_amnt_inv is highly correlated with loan_amnt and 3 other fieldsHigh correlation
installment is highly correlated with loan_amnt and 2 other fieldsHigh correlation
out_prncp_inv is highly correlated with out_prncpHigh correlation
out_prncp is highly correlated with out_prncp_invHigh correlation
total_pymnt is highly correlated with funded_amnt and 2 other fieldsHigh correlation
total_pymnt_inv is highly correlated with funded_amnt_inv and 2 other fieldsHigh correlation
total_rec_prncp is highly correlated with total_pymnt and 1 other fieldsHigh correlation
sub_grade is highly correlated with gradeHigh correlation
grade is highly correlated with sub_gradeHigh correlation
emp_title has 2459 (6.2%) missing values Missing
emp_length has 1075 (2.7%) missing values Missing
pub_rec_bankruptcies has 697 (1.8%) missing values Missing
annual_inc is highly skewed (γ1 = 30.9491846) Skewed
collection_recovery_fee is highly skewed (γ1 = 25.02941842) Skewed
id has unique values Unique
member_id has unique values Unique
url has unique values Unique
delinq_2yrs has 35405 (89.1%) zeros Zeros
inq_last_6mths has 19300 (48.6%) zeros Zeros
pub_rec has 37601 (94.7%) zeros Zeros
revol_bal has 994 (2.5%) zeros Zeros
out_prncp has 38577 (97.1%) zeros Zeros
out_prncp_inv has 38577 (97.1%) zeros Zeros
total_rec_late_fee has 37671 (94.8%) zeros Zeros
recoveries has 35499 (89.4%) zeros Zeros
collection_recovery_fee has 35935 (90.5%) zeros Zeros

Variables

id
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct count39717
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean683131.9130598988
Minimum54734
Maximum1077501
Zeros0
Zeros (%)0.0%
Memory size310.3 KiB
2020-08-28T11:31:31.254315image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum54734
5-th percentile372418.4
Q1516221
median665665
Q3837755
95-th percentile1039966.2
Maximum1077501
Range1022767
Interquartile range (IQR)321534

Descriptive statistics

Standard deviation210694.1329
Coefficient of variation (CV)0.3084237889
Kurtosis-0.7298893992
Mean683131.9131
Median Absolute Deviation (MAD)160026
Skewness0.07880763188
Sum2.713195019e+10
Variance4.439201764e+10
2020-08-28T11:31:31.330700image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
10526701< 0.1%
 
6855401< 0.1%
 
2534251< 0.1%
 
7306061< 0.1%
 
8678211< 0.1%
 
9866031< 0.1%
 
4448811< 0.1%
 
6495781< 0.1%
 
8780541< 0.1%
 
2647921< 0.1%
 
Other values (39707)39707> 99.9%
 
ValueCountFrequency (%) 
547341< 0.1%
 
557421< 0.1%
 
572451< 0.1%
 
574161< 0.1%
 
589151< 0.1%
 
ValueCountFrequency (%) 
10775011< 0.1%
 
10774301< 0.1%
 
10771751< 0.1%
 
10768631< 0.1%
 
10753581< 0.1%
 

member_id
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct count39717
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean850463.5594078102
Minimum70699
Maximum1314167
Zeros0
Zeros (%)0.0%
Memory size310.3 KiB
2020-08-28T11:31:31.419592image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum70699
5-th percentile388192.4
Q1666780
median850812
Q31047339
95-th percentile1269461.8
Maximum1314167
Range1243468
Interquartile range (IQR)380559

Descriptive statistics

Standard deviation265678.3074
Coefficient of variation (CV)0.3123923471
Kurtosis-0.5629680132
Mean850463.5594
Median Absolute Deviation (MAD)190427
Skewness-0.2124163719
Sum3.377786119e+10
Variance7.058496303e+10
2020-08-28T11:31:31.496686image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
7455841< 0.1%
 
9005411< 0.1%
 
6347451< 0.1%
 
9435581< 0.1%
 
8969581< 0.1%
 
7408031< 0.1%
 
9415051< 0.1%
 
7204621< 0.1%
 
9599341< 0.1%
 
4999011< 0.1%
 
Other values (39707)39707> 99.9%
 
ValueCountFrequency (%) 
706991< 0.1%
 
736731< 0.1%
 
747241< 0.1%
 
765831< 0.1%
 
803531< 0.1%
 
ValueCountFrequency (%) 
13141671< 0.1%
 
13135241< 0.1%
 
13117481< 0.1%
 
13114411< 0.1%
 
13069571< 0.1%
 

loan_amnt
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count885
Unique (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11219.443814991062
Minimum500
Maximum35000
Zeros0
Zeros (%)0.0%
Memory size310.3 KiB
2020-08-28T11:31:31.586348image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile2400
Q15500
median10000
Q315000
95-th percentile25000
Maximum35000
Range34500
Interquartile range (IQR)9500

Descriptive statistics

Standard deviation7456.670694
Coefficient of variation (CV)0.6646203517
Kurtosis0.7686685518
Mean11219.44381
Median Absolute Deviation (MAD)5000
Skewness1.05931729
Sum445602650
Variance55601937.84
2020-08-28T11:31:31.675275image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1000028337.1%
 
1200023345.9%
 
500020515.2%
 
600019084.8%
 
1500018954.8%
 
2000016264.1%
 
800015864.0%
 
2500013903.5%
 
400011302.8%
 
300010302.6%
 
Other values (875)2193455.2%
 
ValueCountFrequency (%) 
5005< 0.1%
 
7001< 0.1%
 
7251< 0.1%
 
7501< 0.1%
 
8001< 0.1%
 
ValueCountFrequency (%) 
350006791.7%
 
348002< 0.1%
 
346751< 0.1%
 
345251< 0.1%
 
344755< 0.1%
 

funded_amnt
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count1041
Unique (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10947.713195860715
Minimum500
Maximum35000
Zeros0
Zeros (%)0.0%
Memory size310.3 KiB
2020-08-28T11:31:31.762568image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile2400
Q15400
median9600
Q315000
95-th percentile25000
Maximum35000
Range34500
Interquartile range (IQR)9600

Descriptive statistics

Standard deviation7187.23867
Coefficient of variation (CV)0.6565059334
Kurtosis0.9375519943
Mean10947.7132
Median Absolute Deviation (MAD)4600
Skewness1.081710238
Sum434810325
Variance51656399.7
2020-08-28T11:31:31.864618image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1000027416.9%
 
1200022445.6%
 
500020405.1%
 
600018984.8%
 
1500017844.5%
 
800015734.0%
 
2000014563.7%
 
2500011332.9%
 
400011272.8%
 
300010222.6%
 
Other values (1031)2269957.2%
 
ValueCountFrequency (%) 
5005< 0.1%
 
7001< 0.1%
 
7251< 0.1%
 
7501< 0.1%
 
8001< 0.1%
 
ValueCountFrequency (%) 
350005541.4%
 
348001< 0.1%
 
346752< 0.1%
 
345251< 0.1%
 
344754< 0.1%
 

funded_amnt_inv
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count8205
Unique (%)20.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10397.448867750922
Minimum0.0
Maximum35000.0
Zeros129
Zeros (%)0.3%
Memory size310.3 KiB
2020-08-28T11:31:31.959556image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1873.658
Q15000
median8975
Q314400
95-th percentile24736.57226
Maximum35000
Range35000
Interquartile range (IQR)9400

Descriptive statistics

Standard deviation7128.450439
Coefficient of variation (CV)0.6855961044
Kurtosis1.062544362
Mean10397.44887
Median Absolute Deviation (MAD)4200
Skewness1.106212938
Sum412955476.7
Variance50814805.66
2020-08-28T11:31:32.044182image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
500013093.3%
 
1000012753.2%
 
600012003.0%
 
1200010692.7%
 
80009002.3%
 
40008122.0%
 
30008032.0%
 
150006571.7%
 
70006001.5%
 
20004521.1%
 
Other values (8195)3064077.1%
 
ValueCountFrequency (%) 
01290.3%
 
0.0001210981< 0.1%
 
0.0005311331< 0.1%
 
0.0006546071< 0.1%
 
0.0018676961< 0.1%
 
ValueCountFrequency (%) 
350001350.3%
 
34997.352451< 0.1%
 
34993.655391< 0.1%
 
34993.325711< 0.1%
 
34993.263061< 0.1%
 

term
Categorical

Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size310.3 KiB
36 months
29096
60 months
10621
ValueCountFrequency (%) 
36 months2909673.3%
 
60 months1062126.7%
 
2020-08-28T11:31:32.296336image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

int_rate
Categorical

HIGH CARDINALITY

Distinct count371
Unique (%)0.9%
Missing0
Missing (%)0.0%
Memory size310.3 KiB
10.99%
 
956
13.49%
 
826
11.49%
 
825
7.51%
 
787
7.88%
 
725
Other values (366)
35598
ValueCountFrequency (%) 
10.99%9562.4%
 
13.49%8262.1%
 
11.49%8252.1%
 
7.51%7872.0%
 
7.88%7251.8%
 
7.49%6561.7%
 
11.71%6071.5%
 
9.99%6031.5%
 
7.90%5821.5%
 
5.42%5731.4%
 
Other values (361)3257782.0%
 
2020-08-28T11:31:32.544244image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.694287081
Min length5

installment
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count15383
Unique (%)38.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean324.5619220988493
Minimum15.69
Maximum1305.19
Zeros0
Zeros (%)0.0%
Memory size310.3 KiB
2020-08-28T11:31:32.643766image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum15.69
5-th percentile71.246
Q1167.02
median280.22
Q3430.78
95-th percentile762.996
Maximum1305.19
Range1289.5
Interquartile range (IQR)263.76

Descriptive statistics

Standard deviation208.8748735
Coefficient of variation (CV)0.6435593929
Kurtosis1.246801303
Mean324.5619221
Median Absolute Deviation (MAD)123.2
Skewness1.128419095
Sum12890625.86
Variance43628.71279
2020-08-28T11:31:32.720807image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
311.11680.2%
 
180.96590.1%
 
311.02540.1%
 
150.8480.1%
 
368.45460.1%
 
372.12450.1%
 
330.76430.1%
 
339.31420.1%
 
301.6410.1%
 
186.61410.1%
 
Other values (15373)3923098.8%
 
ValueCountFrequency (%) 
15.691< 0.1%
 
16.081< 0.1%
 
16.251< 0.1%
 
16.311< 0.1%
 
16.471< 0.1%
 
ValueCountFrequency (%) 
1305.191< 0.1%
 
1302.691< 0.1%
 
1295.211< 0.1%
 
1288.12< 0.1%
 
1283.51< 0.1%
 

grade
Categorical

HIGH CORRELATION

Distinct count7
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size310.3 KiB
B
12020
A
10085
C
8098
D
5307
E
2842
Other values (2)
 
1365
ValueCountFrequency (%) 
B1202030.3%
 
A1008525.4%
 
C809820.4%
 
D530713.4%
 
E28427.2%
 
F10492.6%
 
G3160.8%
 
2020-08-28T11:31:32.942908image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

sub_grade
Categorical

HIGH CORRELATION

Distinct count35
Unique (%)0.1%
Missing0
Missing (%)0.0%
Memory size310.3 KiB
B3
 
2917
A4
 
2886
A5
 
2742
B5
 
2704
B4
 
2512
Other values (30)
25956
ValueCountFrequency (%) 
B329177.3%
 
A428867.3%
 
A527426.9%
 
B527046.8%
 
B425126.3%
 
C121365.4%
 
B220575.2%
 
C220115.1%
 
B118304.6%
 
A318104.6%
 
Other values (25)1611240.6%
 
2020-08-28T11:31:33.220005image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

emp_title
Categorical

HIGH CARDINALITY
MISSING

Distinct count28820
Unique (%)77.4%
Missing2459
Missing (%)6.2%
Memory size310.3 KiB
US Army
 
134
Bank of America
 
109
IBM
 
66
AT&T
 
59
Kaiser Permanente
 
56
Other values (28815)
36834
ValueCountFrequency (%) 
US Army1340.3%
 
Bank of America1090.3%
 
IBM660.2%
 
AT&T590.1%
 
Kaiser Permanente560.1%
 
USAF540.1%
 
Wells Fargo540.1%
 
UPS530.1%
 
US Air Force520.1%
 
Walmart450.1%
 
Other values (28810)3657692.1%
 
(Missing)24596.2%
 
2020-08-28T11:31:33.591257image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length78
Median length17
Mean length17.42757509
Min length2

emp_length
Categorical

MISSING

Distinct count11
Unique (%)< 0.1%
Missing1075
Missing (%)2.7%
Memory size310.3 KiB
10+ years
8879
< 1 year
4583
2 years
4388
3 years
4095
4 years
3436
Other values (6)
13261
ValueCountFrequency (%) 
10+ years887922.4%
 
< 1 year458311.5%
 
2 years438811.0%
 
3 years409510.3%
 
4 years34368.7%
 
5 years32828.3%
 
1 year32408.2%
 
6 years22295.6%
 
7 years17734.5%
 
8 years14793.7%
 
2020-08-28T11:31:33.866716image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length9
Median length7
Mean length7.372661581
Min length3

home_ownership
Categorical

Distinct count5
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size310.3 KiB
RENT
18899
MORTGAGE
17659
OWN
 
3058
OTHER
 
98
NONE
 
3
ValueCountFrequency (%) 
RENT1889947.6%
 
MORTGAGE1765944.5%
 
OWN30587.7%
 
OTHER980.2%
 
NONE3< 0.1%
 
2020-08-28T11:31:34.143692image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length8
Median length4
Mean length5.703955485
Min length3

annual_inc
Real number (ℝ≥0)

SKEWED

Distinct count5318
Unique (%)13.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68968.92637711811
Minimum4000.0
Maximum6000000.0
Zeros0
Zeros (%)0.0%
Memory size310.3 KiB
2020-08-28T11:31:34.227821image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum4000
5-th percentile24000
Q140404
median59000
Q382300
95-th percentile142000
Maximum6000000
Range5996000
Interquartile range (IQR)41896

Descriptive statistics

Standard deviation63793.76579
Coefficient of variation (CV)0.9249638807
Kurtosis2302.737777
Mean68968.92638
Median Absolute Deviation (MAD)20000
Skewness30.9491846
Sum2739238849
Variance4069644554
2020-08-28T11:31:34.316576image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
6000015053.8%
 
5000010572.7%
 
400008762.2%
 
450008302.1%
 
300008252.1%
 
750008112.0%
 
650008032.0%
 
700007331.8%
 
480007231.8%
 
800006621.7%
 
Other values (5308)3089277.8%
 
ValueCountFrequency (%) 
40001< 0.1%
 
40801< 0.1%
 
42002< 0.1%
 
48004< 0.1%
 
48881< 0.1%
 
ValueCountFrequency (%) 
60000001< 0.1%
 
39000001< 0.1%
 
20397841< 0.1%
 
19000001< 0.1%
 
17820001< 0.1%
 
Distinct count3
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size310.3 KiB
Not Verified
16921
Verified
12809
Source Verified
9987
ValueCountFrequency (%) 
Not Verified1692142.6%
 
Verified1280932.3%
 
Source Verified998725.1%
 
2020-08-28T11:31:34.549196image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length15
Median length12
Mean length11.46433517
Min length8

issue_d
Categorical

HIGH CARDINALITY

Distinct count55
Unique (%)0.1%
Missing0
Missing (%)0.0%
Memory size310.3 KiB
Dec-11
 
2260
Nov-11
 
2223
Oct-11
 
2114
Sep-11
 
2063
Aug-11
 
1928
Other values (50)
29129
ValueCountFrequency (%) 
Dec-1122605.7%
 
Nov-1122235.6%
 
Oct-1121145.3%
 
Sep-1120635.2%
 
Aug-1119284.9%
 
Jul-1118704.7%
 
Jun-1118274.6%
 
May-1116894.3%
 
Apr-1115623.9%
 
Mar-1114433.6%
 
Other values (45)2073852.2%
 
2020-08-28T11:31:34.780001image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

loan_status
Categorical

Distinct count3
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size310.3 KiB
Fully Paid
32950
Charged Off
 
5627
Current
 
1140
ValueCountFrequency (%) 
Fully Paid3295083.0%
 
Charged Off562714.2%
 
Current11402.9%
 
2020-08-28T11:31:35.008693image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length11
Median length10
Mean length10.05556814
Min length7

pymnt_plan
Categorical

CONSTANT
REJECTED

Distinct count1
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size310.3 KiB
n
39717
ValueCountFrequency (%) 
n39717100.0%
 
2020-08-28T11:31:35.270278image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

url
URL

UNIQUE

Distinct count39717
Unique (%)100.0%
Missing0
Missing (%)0.0%
Memory size310.3 KiB
https://lendingclub.com/browse/loanDetail.action?loan_id=876837
 
1
https://lendingclub.com/browse/loanDetail.action?loan_id=621226
 
1
https://lendingclub.com/browse/loanDetail.action?loan_id=775126
 
1
https://lendingclub.com/browse/loanDetail.action?loan_id=450346
 
1
https://lendingclub.com/browse/loanDetail.action?loan_id=550106
 
1
Other values (39712)
39712
ValueCountFrequency (%) 
https://lendingclub.com/browse/loanDetail.action?loan_id=8768371< 0.1%
 
https://lendingclub.com/browse/loanDetail.action?loan_id=6212261< 0.1%
 
https://lendingclub.com/browse/loanDetail.action?loan_id=7751261< 0.1%
 
https://lendingclub.com/browse/loanDetail.action?loan_id=4503461< 0.1%
 
https://lendingclub.com/browse/loanDetail.action?loan_id=5501061< 0.1%
 
https://lendingclub.com/browse/loanDetail.action?loan_id=6958761< 0.1%
 
https://lendingclub.com/browse/loanDetail.action?loan_id=4457681< 0.1%
 
https://lendingclub.com/browse/loanDetail.action?loan_id=7068131< 0.1%
 
https://lendingclub.com/browse/loanDetail.action?loan_id=10541651< 0.1%
 
https://lendingclub.com/browse/loanDetail.action?loan_id=6348251< 0.1%
 
Other values (39707)39707> 99.9%
 
ValueCountFrequency (%) 
https39717100.0%
 
ValueCountFrequency (%) 
lendingclub.com39717100.0%
 
ValueCountFrequency (%) 
/browse/loanDetail.action39717100.0%
 
ValueCountFrequency (%) 
loan_id=5519571< 0.1%
 
loan_id=8292641< 0.1%
 
loan_id=8553111< 0.1%
 
loan_id=4574141< 0.1%
 
loan_id=10225791< 0.1%
 
loan_id=7215941< 0.1%
 
loan_id=8895951< 0.1%
 
loan_id=10007861< 0.1%
 
loan_id=10570241< 0.1%
 
loan_id=6174311< 0.1%
 
Other values (39707)39707> 99.9%
 
ValueCountFrequency (%) 
39717100.0%
 

purpose
Categorical

Distinct count14
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size310.3 KiB
debt_consolidation
18641
credit_card
5130
other
3993
home_improvement
 
2976
major_purchase
 
2187
Other values (9)
6790
ValueCountFrequency (%) 
debt_consolidation1864146.9%
 
credit_card513012.9%
 
other399310.1%
 
home_improvement29767.5%
 
major_purchase21875.5%
 
small_business18284.6%
 
car15493.9%
 
wedding9472.4%
 
medical6931.7%
 
moving5831.5%
 
Other values (4)11903.0%
 
2020-08-28T11:31:35.573107image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length18
Median length16
Mean length13.7361835
Min length3

title
Categorical

HIGH CARDINALITY

Distinct count19615
Unique (%)49.4%
Missing11
Missing (%)< 0.1%
Memory size310.3 KiB
Debt Consolidation
 
2184
Debt Consolidation Loan
 
1729
Personal Loan
 
659
Consolidation
 
517
debt consolidation
 
505
Other values (19610)
34112
ValueCountFrequency (%) 
Debt Consolidation21845.5%
 
Debt Consolidation Loan17294.4%
 
Personal Loan6591.7%
 
Consolidation5171.3%
 
debt consolidation5051.3%
 
Home Improvement3560.9%
 
Credit Card Consolidation3560.9%
 
Debt consolidation3340.8%
 
Small Business Loan3280.8%
 
Credit Card Loan3170.8%
 
Other values (19605)3242181.6%
 
2020-08-28T11:31:35.840965image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length80
Median length16
Mean length17.18339754
Min length1

zip_code
Categorical

HIGH CARDINALITY

Distinct count823
Unique (%)2.1%
Missing0
Missing (%)0.0%
Memory size310.3 KiB
100xx
 
597
945xx
 
545
112xx
 
516
606xx
 
503
070xx
 
473
Other values (818)
37083
ValueCountFrequency (%) 
100xx5971.5%
 
945xx5451.4%
 
112xx5161.3%
 
606xx5031.3%
 
070xx4731.2%
 
900xx4531.1%
 
021xx3971.0%
 
300xx3941.0%
 
926xx3710.9%
 
750xx3670.9%
 
Other values (813)3510188.4%
 
2020-08-28T11:31:36.214367image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

addr_state
Categorical

Distinct count50
Unique (%)0.1%
Missing0
Missing (%)0.0%
Memory size310.3 KiB
CA
7099
NY
 
3812
FL
 
2866
TX
 
2727
NJ
 
1850
Other values (45)
21363
ValueCountFrequency (%) 
CA709917.9%
 
NY38129.6%
 
FL28667.2%
 
TX27276.9%
 
NJ18504.7%
 
IL15253.8%
 
PA15173.8%
 
VA14073.5%
 
GA13983.5%
 
MA13403.4%
 
Other values (40)1417635.7%
 
2020-08-28T11:31:36.446924image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

dti
Real number (ℝ≥0)

Distinct count2868
Unique (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.315129541506153
Minimum0.0
Maximum29.99
Zeros183
Zeros (%)0.5%
Memory size310.3 KiB
2020-08-28T11:31:36.577977image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.13
Q18.17
median13.4
Q318.6
95-th percentile23.84
Maximum29.99
Range29.99
Interquartile range (IQR)10.43

Descriptive statistics

Standard deviation6.678593595
Coefficient of variation (CV)0.501579318
Kurtosis-0.8520154806
Mean13.31512954
Median Absolute Deviation (MAD)5.21
Skewness-0.02804333095
Sum528837
Variance44.6036124
2020-08-28T11:31:36.675955image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
01830.5%
 
12510.1%
 
18450.1%
 
19.2400.1%
 
13.2390.1%
 
13.5380.1%
 
12.48380.1%
 
16.8380.1%
 
6370.1%
 
14.29360.1%
 
Other values (2858)3917298.6%
 
ValueCountFrequency (%) 
01830.5%
 
0.013< 0.1%
 
0.025< 0.1%
 
0.032< 0.1%
 
0.043< 0.1%
 
ValueCountFrequency (%) 
29.991< 0.1%
 
29.951< 0.1%
 
29.933< 0.1%
 
29.922< 0.1%
 
29.891< 0.1%
 

delinq_2yrs
Real number (ℝ≥0)

ZEROS

Distinct count11
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1465115693531737
Minimum0
Maximum11
Zeros35405
Zeros (%)89.1%
Memory size310.3 KiB
2020-08-28T11:31:36.756938image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum11
Range11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.491811516
Coefficient of variation (CV)3.356810102
Kurtosis39.41249957
Mean0.1465115694
Median Absolute Deviation (MAD)0
Skewness5.022035213
Sum5819
Variance0.2418785673
2020-08-28T11:31:36.835159image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
03540589.1%
 
133038.3%
 
26871.7%
 
32200.6%
 
4620.2%
 
5220.1%
 
610< 0.1%
 
74< 0.1%
 
82< 0.1%
 
111< 0.1%
 
ValueCountFrequency (%) 
03540589.1%
 
133038.3%
 
26871.7%
 
32200.6%
 
4620.2%
 
ValueCountFrequency (%) 
111< 0.1%
 
91< 0.1%
 
82< 0.1%
 
74< 0.1%
 
610< 0.1%
 

earliest_cr_line
Categorical

HIGH CARDINALITY

Distinct count526
Unique (%)1.3%
Missing0
Missing (%)0.0%
Memory size310.3 KiB
Nov-98
 
370
Oct-99
 
366
Dec-98
 
348
Oct-00
 
346
Dec-97
 
329
Other values (521)
37958
ValueCountFrequency (%) 
Nov-983700.9%
 
Oct-993660.9%
 
Dec-983480.9%
 
Oct-003460.9%
 
Dec-973290.8%
 
Nov-003200.8%
 
Nov-993190.8%
 
Sep-003060.8%
 
Oct-983050.8%
 
Nov-972980.8%
 
Other values (516)3641091.7%
 
2020-08-28T11:31:37.061197image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

inq_last_6mths
Real number (ℝ≥0)

ZEROS

Distinct count9
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8691995870785809
Minimum0
Maximum8
Zeros19300
Zeros (%)48.6%
Memory size310.3 KiB
2020-08-28T11:31:37.143398image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile3
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.070219332
Coefficient of variation (CV)1.23126995
Kurtosis2.562159858
Mean0.8691995871
Median Absolute Deviation (MAD)1
Skewness1.390390927
Sum34522
Variance1.145369419
2020-08-28T11:31:37.240302image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
01930048.6%
 
11097127.6%
 
2581214.6%
 
330487.7%
 
43260.8%
 
51460.4%
 
6640.2%
 
7350.1%
 
815< 0.1%
 
ValueCountFrequency (%) 
01930048.6%
 
11097127.6%
 
2581214.6%
 
330487.7%
 
43260.8%
 
ValueCountFrequency (%) 
815< 0.1%
 
7350.1%
 
6640.2%
 
51460.4%
 
43260.8%
 

open_acc
Real number (ℝ≥0)

Distinct count40
Unique (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.294407936148248
Minimum2
Maximum44
Zeros0
Zeros (%)0.0%
Memory size310.3 KiB
2020-08-28T11:31:37.333949image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q16
median9
Q312
95-th percentile17
Maximum44
Range42
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.400282474
Coefficient of variation (CV)0.4734333272
Kurtosis1.67757203
Mean9.294407936
Median Absolute Deviation (MAD)3
Skewness1.00376191
Sum369146
Variance19.36248585
2020-08-28T11:31:37.410779image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
7401810.1%
 
639469.9%
 
839369.9%
 
937189.4%
 
1032238.1%
 
531838.0%
 
1127466.9%
 
423435.9%
 
1222735.7%
 
1319114.8%
 
Other values (30)842021.2%
 
ValueCountFrequency (%) 
26051.5%
 
314933.8%
 
423435.9%
 
531838.0%
 
639469.9%
 
ValueCountFrequency (%) 
441< 0.1%
 
421< 0.1%
 
411< 0.1%
 
391< 0.1%
 
381< 0.1%
 

pub_rec
Real number (ℝ≥0)

ZEROS

Distinct count5
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.055064581917063225
Minimum0
Maximum4
Zeros37601
Zeros (%)94.7%
Memory size310.3 KiB
2020-08-28T11:31:37.497976image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum4
Range4
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2371999517
Coefficient of variation (CV)4.307668259
Kurtosis23.88943797
Mean0.05506458192
Median Absolute Deviation (MAD)0
Skewness4.538051316
Sum2187
Variance0.05626381708
2020-08-28T11:31:37.580455image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
03760194.7%
 
120565.2%
 
2510.1%
 
37< 0.1%
 
42< 0.1%
 
ValueCountFrequency (%) 
03760194.7%
 
120565.2%
 
2510.1%
 
37< 0.1%
 
42< 0.1%
 
ValueCountFrequency (%) 
42< 0.1%
 
37< 0.1%
 
2510.1%
 
120565.2%
 
03760194.7%
 

revol_bal
Real number (ℝ≥0)

ZEROS

Distinct count21711
Unique (%)54.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13382.528086209935
Minimum0
Maximum149588
Zeros994
Zeros (%)2.5%
Memory size310.3 KiB
2020-08-28T11:31:37.671655image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile321.8
Q13703
median8850
Q317058
95-th percentile41656.4
Maximum149588
Range149588
Interquartile range (IQR)13355

Descriptive statistics

Standard deviation15885.01664
Coefficient of variation (CV)1.186996697
Kurtosis14.89652278
Mean13382.52809
Median Absolute Deviation (MAD)6027
Skewness3.190883683
Sum531513868
Variance252333753.7
2020-08-28T11:31:37.753484image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
09942.5%
 
25514< 0.1%
 
29814< 0.1%
 
112< 0.1%
 
68211< 0.1%
 
399< 0.1%
 
8659< 0.1%
 
11599< 0.1%
 
17639< 0.1%
 
109< 0.1%
 
Other values (21701)3862797.3%
 
ValueCountFrequency (%) 
09942.5%
 
112< 0.1%
 
25< 0.1%
 
36< 0.1%
 
43< 0.1%
 
ValueCountFrequency (%) 
1495881< 0.1%
 
1495271< 0.1%
 
1490001< 0.1%
 
1488291< 0.1%
 
1488041< 0.1%
 

revol_util
Categorical

HIGH CARDINALITY

Distinct count1089
Unique (%)2.7%
Missing50
Missing (%)0.1%
Memory size310.3 KiB
0%
 
977
0.20%
 
63
63%
 
62
66.70%
 
58
40.70%
 
58
Other values (1084)
38449
ValueCountFrequency (%) 
0%9772.5%
 
0.20%630.2%
 
63%620.2%
 
66.70%580.1%
 
40.70%580.1%
 
0.10%580.1%
 
66.60%570.1%
 
46.40%570.1%
 
31.20%570.1%
 
61%570.1%
 
Other values (1079)3816396.1%
 
2020-08-28T11:31:38.029431image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.518745122
Min length2

total_acc
Real number (ℝ≥0)

Distinct count82
Unique (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.08882846136415
Minimum2
Maximum90
Zeros0
Zeros (%)0.0%
Memory size310.3 KiB
2020-08-28T11:31:38.130286image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile7
Q113
median20
Q329
95-th percentile43
Maximum90
Range88
Interquartile range (IQR)16

Descriptive statistics

Standard deviation11.40170855
Coefficient of variation (CV)0.5161753405
Kurtosis0.6937402027
Mean22.08882846
Median Absolute Deviation (MAD)7
Skewness0.8273790855
Sum877302
Variance129.9989579
2020-08-28T11:31:38.210137image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1614713.7%
 
1514623.7%
 
1714573.7%
 
1414453.6%
 
2014283.6%
 
1814223.6%
 
2114123.6%
 
1313853.5%
 
1913413.4%
 
1213253.3%
 
Other values (72)2556964.4%
 
ValueCountFrequency (%) 
24< 0.1%
 
31820.5%
 
44201.1%
 
55521.4%
 
66831.7%
 
ValueCountFrequency (%) 
901< 0.1%
 
871< 0.1%
 
811< 0.1%
 
801< 0.1%
 
792< 0.1%
 

initial_list_status
Categorical

CONSTANT
REJECTED

Distinct count1
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size310.3 KiB
f
39717
ValueCountFrequency (%) 
f39717100.0%
 
2020-08-28T11:31:38.470140image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

out_prncp
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct count1137
Unique (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.22788730266637
Minimum0.0
Maximum6311.47
Zeros38577
Zeros (%)97.1%
Memory size310.3 KiB
2020-08-28T11:31:38.552169image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum6311.47
Range6311.47
Interquartile range (IQR)0

Descriptive statistics

Standard deviation375.1728389
Coefficient of variation (CV)7.323605532
Kurtosis97.6585546
Mean51.2278873
Median Absolute Deviation (MAD)0
Skewness9.226730006
Sum2034618
Variance140754.659
2020-08-28T11:31:38.634284image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
03857797.1%
 
827.132< 0.1%
 
2277.112< 0.1%
 
1972.62< 0.1%
 
2963.242< 0.1%
 
749.321< 0.1%
 
1204.881< 0.1%
 
942.681< 0.1%
 
2812.851< 0.1%
 
2853.891< 0.1%
 
Other values (1127)11272.8%
 
ValueCountFrequency (%) 
03857797.1%
 
10.261< 0.1%
 
11.911< 0.1%
 
13.281< 0.1%
 
19.121< 0.1%
 
ValueCountFrequency (%) 
6311.471< 0.1%
 
6308.371< 0.1%
 
6307.371< 0.1%
 
6307.151< 0.1%
 
6219.161< 0.1%
 

out_prncp_inv
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct count1138
Unique (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.98976810937382
Minimum0.0
Maximum6307.37
Zeros38577
Zeros (%)97.1%
Memory size310.3 KiB
2020-08-28T11:31:38.719455image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum6307.37
Range6307.37
Interquartile range (IQR)0

Descriptive statistics

Standard deviation373.8244569
Coefficient of variation (CV)7.331362169
Kurtosis98.04055348
Mean50.98976811
Median Absolute Deviation (MAD)0
Skewness9.243765495
Sum2025160.62
Variance139744.7246
2020-08-28T11:31:38.801410image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
03857797.1%
 
1972.62< 0.1%
 
827.132< 0.1%
 
1664.642< 0.1%
 
1143.71< 0.1%
 
4527.011< 0.1%
 
2095.611< 0.1%
 
332.471< 0.1%
 
804.391< 0.1%
 
815.071< 0.1%
 
Other values (1128)11282.8%
 
ValueCountFrequency (%) 
03857797.1%
 
10.261< 0.1%
 
11.911< 0.1%
 
13.281< 0.1%
 
19.091< 0.1%
 
ValueCountFrequency (%) 
6307.371< 0.1%
 
6306.961< 0.1%
 
6298.111< 0.1%
 
6276.751< 0.1%
 
6219.161< 0.1%
 

total_pymnt
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count37850
Unique (%)95.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12153.596543630887
Minimum0.0
Maximum58563.67993
Zeros16
Zeros (%)< 0.1%
Memory size310.3 KiB
2020-08-28T11:31:38.893784image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1887.957036
Q15576.93
median9899.640319
Q316534.43304
95-th percentile30245.11853
Maximum58563.67993
Range58563.67993
Interquartile range (IQR)10957.50304

Descriptive statistics

Standard deviation9042.040766
Coefficient of variation (CV)0.743980659
Kurtosis1.985894249
Mean12153.59654
Median Absolute Deviation (MAD)5016.756711
Skewness1.339857366
Sum482704393.9
Variance81758501.21
2020-08-28T11:31:38.972143image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
11196.56943260.1%
 
016< 0.1%
 
11784.2322316< 0.1%
 
10956.7759616< 0.1%
 
13148.1378615< 0.1%
 
5478.38798115< 0.1%
 
13435.9002113< 0.1%
 
5557.02554313< 0.1%
 
13263.9546412< 0.1%
 
5598.28471311< 0.1%
 
Other values (37840)3956499.6%
 
ValueCountFrequency (%) 
016< 0.1%
 
33.731< 0.1%
 
35.711< 0.1%
 
44.922< 0.1%
 
44.961< 0.1%
 
ValueCountFrequency (%) 
58563.679931< 0.1%
 
58480.139921< 0.1%
 
57835.279911< 0.1%
 
56849.269861< 0.1%
 
56662.589941< 0.1%
 

total_pymnt_inv
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count37518
Unique (%)94.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11567.149118009922
Minimum0.0
Maximum58563.68
Zeros165
Zeros (%)0.4%
Memory size310.3 KiB
2020-08-28T11:31:39.065020image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1420.408
Q15112.31
median9287.15
Q315798.81
95-th percentile29627.236
Maximum58563.68
Range58563.68
Interquartile range (IQR)10686.5

Descriptive statistics

Standard deviation8942.672613
Coefficient of variation (CV)0.7731094777
Kurtosis2.029758507
Mean11567.14912
Median Absolute Deviation (MAD)4939.58
Skewness1.35483764
Sum459412461.5
Variance79971393.47
2020-08-28T11:31:39.149133image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
01650.4%
 
6514.5216< 0.1%
 
13148.1414< 0.1%
 
5478.3914< 0.1%
 
11196.5712< 0.1%
 
10956.7812< 0.1%
 
6717.9512< 0.1%
 
7328.9211< 0.1%
 
13517.3611< 0.1%
 
5557.0311< 0.1%
 
Other values (37508)3943999.3%
 
ValueCountFrequency (%) 
01650.4%
 
0.541< 0.1%
 
12.651< 0.1%
 
18.971< 0.1%
 
21.61< 0.1%
 
ValueCountFrequency (%) 
58563.681< 0.1%
 
58438.371< 0.1%
 
57628.731< 0.1%
 
56622.121< 0.1%
 
56515.161< 0.1%
 

total_rec_prncp
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count7976
Unique (%)20.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9793.348813102702
Minimum0.0
Maximum35000.02
Zeros74
Zeros (%)0.2%
Memory size310.3 KiB
2020-08-28T11:31:39.233096image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1339.842
Q14600
median8000
Q313653.26
95-th percentile24999.982
Maximum35000.02
Range35000.02
Interquartile range (IQR)9053.26

Descriptive statistics

Standard deviation7065.522127
Coefficient of variation (CV)0.7214612961
Kurtosis1.103355455
Mean9793.348813
Median Absolute Deviation (MAD)4000
Skewness1.118254546
Sum388962434.8
Variance49921602.93
2020-08-28T11:31:39.315069image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1000022935.8%
 
1200018054.5%
 
500017024.3%
 
600016374.1%
 
1500014003.5%
 
800013183.3%
 
2000010592.7%
 
40009562.4%
 
30008832.2%
 
70008512.1%
 
Other values (7966)2581365.0%
 
ValueCountFrequency (%) 
0740.2%
 
21.211< 0.1%
 
21.931< 0.1%
 
22.241< 0.1%
 
22.51< 0.1%
 
ValueCountFrequency (%) 
35000.022< 0.1%
 
35000.011< 0.1%
 
350003630.9%
 
34999.995< 0.1%
 
34999.981< 0.1%
 

total_rec_int
Real number (ℝ≥0)

Distinct count35148
Unique (%)88.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2263.6631721932677
Minimum0.0
Maximum23563.68
Zeros71
Zeros (%)0.2%
Memory size310.3 KiB
2020-08-28T11:31:39.407487image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile186.168
Q1662.18
median1348.91
Q32833.4
95-th percentile7575.812
Maximum23563.68
Range23563.68
Interquartile range (IQR)2171.22

Descriptive statistics

Standard deviation2608.111964
Coefficient of variation (CV)1.152164331
Kurtosis9.688278395
Mean2263.663172
Median Absolute Deviation (MAD)866.01
Skewness2.668747187
Sum89905910.21
Variance6802248.019
2020-08-28T11:31:39.476960image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0710.2%
 
1196.57260.1%
 
514.5219< 0.1%
 
1784.2317< 0.1%
 
956.7817< 0.1%
 
1148.1417< 0.1%
 
717.9517< 0.1%
 
478.3916< 0.1%
 
1907.3514< 0.1%
 
557.0313< 0.1%
 
Other values (35138)3949099.4%
 
ValueCountFrequency (%) 
0710.2%
 
6.221< 0.1%
 
6.271< 0.1%
 
7.191< 0.1%
 
7.22< 0.1%
 
ValueCountFrequency (%) 
23563.681< 0.1%
 
23506.561< 0.1%
 
23480.141< 0.1%
 
22835.281< 0.1%
 
22716.421< 0.1%
 

total_rec_late_fee
Real number (ℝ≥0)

ZEROS

Distinct count1356
Unique (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3630152124181836
Minimum0.0
Maximum180.2
Zeros37671
Zeros (%)94.8%
Memory size310.3 KiB
2020-08-28T11:31:39.548917image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile14.924199
Maximum180.2
Range180.2
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.289979302
Coefficient of variation (CV)5.348421085
Kurtosis100.8515437
Mean1.363015212
Median Absolute Deviation (MAD)0
Skewness8.429536
Sum54134.87519
Variance53.14379822
2020-08-28T11:31:39.631815image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
03767194.8%
 
152550.6%
 
15.00000001580.1%
 
30550.1%
 
15.00000002470.1%
 
14.99999999400.1%
 
14.99999998330.1%
 
15.00000003320.1%
 
15.00000004250.1%
 
14.99999997250.1%
 
Other values (1346)14763.7%
 
ValueCountFrequency (%) 
03767194.8%
 
0.011< 0.1%
 
0.0607997511< 0.1%
 
0.0737871041< 0.1%
 
0.1017045621< 0.1%
 
ValueCountFrequency (%) 
180.21< 0.1%
 
166.42971071< 0.1%
 
165.691< 0.1%
 
146.60000031< 0.1%
 
146.041< 0.1%
 

recoveries
Real number (ℝ≥0)

ZEROS

Distinct count4040
Unique (%)10.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean95.22162387467331
Minimum0.0
Maximum29623.35
Zeros35499
Zeros (%)89.4%
Memory size310.3 KiB
2020-08-28T11:31:39.735349image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile362.418
Maximum29623.35
Range29623.35
Interquartile range (IQR)0

Descriptive statistics

Standard deviation688.744771
Coefficient of variation (CV)7.23307105
Kurtosis379.3775773
Mean95.22162387
Median Absolute Deviation (MAD)0
Skewness16.5193782
Sum3781917.235
Variance474369.3595
2020-08-28T11:31:39.819220image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
03549989.4%
 
10.44< 0.1%
 
11.294< 0.1%
 
19.23< 0.1%
 
13.593< 0.1%
 
11.23< 0.1%
 
10.663< 0.1%
 
13.933< 0.1%
 
13.443< 0.1%
 
44.923< 0.1%
 
Other values (4030)418910.5%
 
ValueCountFrequency (%) 
03549989.4%
 
6.31< 0.1%
 
6.311< 0.1%
 
8.191< 0.1%
 
8.361< 0.1%
 
ValueCountFrequency (%) 
29623.351< 0.1%
 
22943.371< 0.1%
 
21810.311< 0.1%
 
20006.531< 0.1%
 
19915.671< 0.1%
 

collection_recovery_fee
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct count2616
Unique (%)6.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.406111894151396
Minimum0.0
Maximum7002.19
Zeros35935
Zeros (%)90.5%
Memory size310.3 KiB
2020-08-28T11:31:39.902830image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5.152
Maximum7002.19
Range7002.19
Interquartile range (IQR)0

Descriptive statistics

Standard deviation148.6715935
Coefficient of variation (CV)11.98373791
Kurtosis821.3006591
Mean12.40611189
Median Absolute Deviation (MAD)0
Skewness25.02941842
Sum492733.5461
Variance22103.2427
2020-08-28T11:31:39.985542image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
03593590.5%
 
212< 0.1%
 
1.210< 0.1%
 
3.719< 0.1%
 
1.888< 0.1%
 
2.528< 0.1%
 
2.028< 0.1%
 
1.218< 0.1%
 
1.698< 0.1%
 
1.68< 0.1%
 
Other values (2606)37039.3%
 
ValueCountFrequency (%) 
03593590.5%
 
0.0631< 0.1%
 
0.0745000011< 0.1%
 
0.1347999951< 0.1%
 
0.13931< 0.1%
 
ValueCountFrequency (%) 
7002.191< 0.1%
 
6972.591< 0.1%
 
6543.041< 0.1%
 
5774.81< 0.1%
 
5602.721< 0.1%
 

last_pymnt_d
Categorical

HIGH CARDINALITY

Distinct count101
Unique (%)0.3%
Missing71
Missing (%)0.2%
Memory size310.3 KiB
May-16
 
1256
Mar-13
 
1026
Dec-14
 
945
May-13
 
907
Feb-13
 
869
Other values (96)
34643
ValueCountFrequency (%) 
May-1612563.2%
 
Mar-1310262.6%
 
Dec-149452.4%
 
May-139072.3%
 
Feb-138692.2%
 
Apr-138512.1%
 
Mar-128442.1%
 
Aug-148322.1%
 
Jan-148322.1%
 
Aug-128322.1%
 
Other values (91)3045276.7%
 
2020-08-28T11:31:40.208464image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.994637057
Min length3

last_pymnt_amnt
Real number (ℝ≥0)

Distinct count34930
Unique (%)87.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2678.826162348616
Minimum0.0
Maximum36115.2
Zeros74
Zeros (%)0.2%
Memory size310.3 KiB
2020-08-28T11:31:40.298371image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile43.34
Q1218.68
median546.14
Q33293.16
95-th percentile12183.944
Maximum36115.2
Range36115.2
Interquartile range (IQR)3074.48

Descriptive statistics

Standard deviation4447.136012
Coefficient of variation (CV)1.660106234
Kurtosis8.867819694
Mean2678.826162
Median Absolute Deviation (MAD)449.45
Skewness2.712122241
Sum106394938.7
Variance19777018.71
2020-08-28T11:31:40.378193image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0740.2%
 
276.06210.1%
 
20017< 0.1%
 
5016< 0.1%
 
10015< 0.1%
 
40012< 0.1%
 
773.4412< 0.1%
 
786.0111< 0.1%
 
15011< 0.1%
 
50011< 0.1%
 
Other values (34920)3951799.5%
 
ValueCountFrequency (%) 
0740.2%
 
0.011< 0.1%
 
0.021< 0.1%
 
0.031< 0.1%
 
0.131< 0.1%
 
ValueCountFrequency (%) 
36115.21< 0.1%
 
35613.681< 0.1%
 
35596.411< 0.1%
 
35479.891< 0.1%
 
35471.861< 0.1%
 

last_credit_pull_d
Categorical

HIGH CARDINALITY

Distinct count106
Unique (%)0.3%
Missing2
Missing (%)< 0.1%
Memory size310.3 KiB
May-16
10308
Apr-16
 
2547
Mar-16
 
1123
Feb-13
 
843
Feb-16
 
736
Other values (101)
24158
ValueCountFrequency (%) 
May-161030826.0%
 
Apr-1625476.4%
 
Mar-1611232.8%
 
Feb-138432.1%
 
Feb-167361.9%
 
Jan-166571.7%
 
Dec-156471.6%
 
Mar-135771.5%
 
Mar-145641.4%
 
Dec-145621.4%
 
Other values (96)2115153.3%
 
2020-08-28T11:31:40.600868image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.999848931
Min length3
Distinct count1
Unique (%)< 0.1%
Missing56
Missing (%)0.1%
Memory size310.3 KiB
0
39661
(Missing)
 
56
ValueCountFrequency (%) 
03966199.9%
 
(Missing)560.1%
 

policy_code
Boolean

CONSTANT
REJECTED

Distinct count1
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size310.3 KiB
1
39717
ValueCountFrequency (%) 
139717100.0%
 

application_type
Categorical

CONSTANT
REJECTED

Distinct count1
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size310.3 KiB
INDIVIDUAL
39717
ValueCountFrequency (%) 
INDIVIDUAL39717100.0%
 
2020-08-28T11:31:40.821307image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

acc_now_delinq
Boolean

CONSTANT
REJECTED

Distinct count1
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size310.3 KiB
0
39717
ValueCountFrequency (%) 
039717100.0%
 
Distinct count1
Unique (%)< 0.1%
Missing56
Missing (%)0.1%
Memory size310.3 KiB
0
39661
(Missing)
 
56
ValueCountFrequency (%) 
03966199.9%
 
(Missing)560.1%
 

delinq_amnt
Boolean

CONSTANT
REJECTED

Distinct count1
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size310.3 KiB
0
39717
ValueCountFrequency (%) 
039717100.0%
 

pub_rec_bankruptcies
Categorical

MISSING

Distinct count3
Unique (%)< 0.1%
Missing697
Missing (%)1.8%
Memory size310.3 KiB
0
37339
1
 
1674
2
 
7
ValueCountFrequency (%) 
03733994.0%
 
116744.2%
 
27< 0.1%
 
(Missing)6971.8%
 
2020-08-28T11:31:41.044383image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

tax_liens
Boolean

Distinct count1
Unique (%)< 0.1%
Missing39
Missing (%)0.1%
Memory size310.3 KiB
0
39678
(Missing)
 
39
ValueCountFrequency (%) 
03967899.9%
 
(Missing)390.1%
 

Interactions

2020-08-28T11:30:17.712620image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:30:17.824243image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:30:17.925868image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:30:18.031801image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:30:18.136303image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:30:18.245098image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:30:18.358965image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:30:18.472020image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:30:18.581306image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:30:18.684168image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:30:18.791993image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:30:18.898906image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:30:19.021885image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:30:19.136546image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:30:19.237314image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:30:19.339546image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:30:19.449765image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:30:19.574809image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:30:19.702469image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:30:19.811650image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:30:19.912044image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:30:20.174789image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:30:20.284865image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:30:20.395479image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:30:20.506415image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:30:20.609478image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:30:20.717980image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:30:20.833094image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:30:20.955734image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:30:21.081038image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:30:21.199707image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:30:21.318031image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:30:21.440757image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:30:21.552327image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:30:21.678940image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:30:21.795362image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:30:21.910455image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:30:22.023472image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:30:22.124182image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:30:22.229358image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:30:22.340999image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:30:22.451871image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:30:22.562061image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:30:22.671342image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:30:22.771215image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:30:22.879276image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:30:22.984398image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:30:23.090849image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:30:23.199374image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:30:23.305880image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:30:23.419183image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:30:23.541247image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:30:23.662661image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:30:23.783306image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:30:23.902825image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:30:24.043298image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:30:24.157963image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:30:24.265090image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-08-28T11:31:12.888249image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:12.991468image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:13.084245image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:13.181259image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:13.280178image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:13.378983image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:13.482516image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:13.585149image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:13.681016image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:13.784339image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:13.885199image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:13.997434image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:14.101683image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:14.205944image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:14.313802image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:14.429082image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:14.540574image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:14.653176image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:14.764575image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:14.875731image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:14.984981image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:15.090667image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:15.201302image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:15.301521image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:15.418304image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:15.531321image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:15.634583image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:15.743207image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:15.852772image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:15.964050image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:16.074823image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:16.187623image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:16.291278image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:16.402756image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:16.512618image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:16.624189image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:16.738151image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:16.839636image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:16.949179image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:17.059300image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:17.167537image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:17.275751image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:17.381385image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:17.488917image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:17.592955image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:17.695827image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:17.805791image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:17.907270image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:18.023412image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:18.135070image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:18.235128image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:18.340451image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:18.450554image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:18.557173image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:18.664114image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:18.776878image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:18.878351image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:18.989606image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:19.095551image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:19.203114image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:19.311659image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:19.421213image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:19.535382image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:19.651540image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:19.766973image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:19.883517image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:19.995643image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:20.109181image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:20.216006image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:20.325709image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:20.456815image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:20.565628image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:20.682602image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:20.794207image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:20.896940image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:21.010118image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:21.122375image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:21.251474image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:21.370908image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:21.503173image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:21.625948image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:21.753354image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:21.873990image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:21.998358image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:22.121134image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:22.236455image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:22.357640image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:22.482124image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:23.069397image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:23.197128image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:23.322288image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:23.441803image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:23.555948image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:23.670097image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:23.827397image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:24.013106image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:24.158598image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:24.324868image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:24.450886image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:24.569108image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:24.694022image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:24.823766image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:24.949426image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:25.071092image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:25.189916image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:25.311347image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:25.430416image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:25.550083image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-08-28T11:31:41.175236image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-08-28T11:31:41.512410image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-08-28T11:31:41.839406image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-08-28T11:31:42.185104image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-08-28T11:31:42.492172image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-08-28T11:31:26.047232image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:29.169504image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:30.381121image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-28T11:31:30.736638image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

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110774301314167250025002500.060 months15.27%59.83CC4Ryder< 1 yearRENT30000.0Source VerifiedDec-11Charged Offnhttps://lendingclub.com/browse/loanDetail.action?loan_id=1077430carbike309xxGA1.000Apr-9953016879.40%4f0.000.001008.7100001008.71456.46435.170.00117.081.11Apr-13119.66Sep-130.01INDIVIDUAL00.000.00.0
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310768631277178100001000010000.036 months13.49%339.31CC1AIR RESOURCES BOARD10+ yearsRENT49200.0Source VerifiedDec-11Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=1076863otherpersonel917xxCA20.000Feb-961100559821%37f0.000.0012231.89000012231.8910000.002214.9216.970.000.00Jan-15357.48Apr-160.01INDIVIDUAL00.000.00.0
410753581311748300030003000.060 months12.69%67.79BB5University Medical Group1 yearRENT80000.0Source VerifiedDec-11Currentnhttps://lendingclub.com/browse/loanDetail.action?loan_id=1075358otherPersonal972xxOR17.940Jan-9601502778353.90%38f524.06524.063513.3300003513.332475.941037.390.000.000.00May-1667.79May-160.01INDIVIDUAL00.000.00.0
510752691311441500050005000.036 months7.90%156.46AA4Veolia Transportaton3 yearsRENT36000.0Source VerifiedDec-11Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=1075269weddingMy wedding loan I promise to pay back852xxAZ11.200Nov-04390796328.30%12f0.000.005632.2100005632.215000.00632.210.000.000.00Jan-15161.03Jan-160.01INDIVIDUAL00.000.00.0
610696391304742700070007000.060 months15.96%170.08CC5Southern Star Photography8 yearsRENT47004.0Not VerifiedDec-11Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=1069639debt_consolidationLoan280xxNC23.510Jul-051701772685.60%11f0.000.0010110.84000010110.846985.613125.230.000.000.00May-161313.76May-160.01INDIVIDUAL00.000.00.0
710720531288686300030003000.036 months18.64%109.43EE1MKC Accounting9 yearsRENT48000.0Source VerifiedDec-11Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=1072053carCar Downpayment900xxCA5.350Jan-07240822187.50%4f0.000.003939.1352943939.143000.00939.140.000.000.00Jan-15111.34Dec-140.01INDIVIDUAL00.000.00.0
810717951306957560056005600.060 months21.28%152.39FF2NaN4 yearsOWN40000.0Source VerifiedDec-11Charged Offnhttps://lendingclub.com/browse/loanDetail.action?loan_id=1071795small_businessExpand Business & Buy Debt Portfolio958xxCA5.550Apr-042110521032.60%13f0.000.00646.020000646.02162.02294.940.00189.062.09Apr-12152.39Aug-120.01INDIVIDUAL00.000.00.0
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Last rows

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39707926669266150005000525.036 months9.33%159.77BB3Stark and Roth Inc2 yearsMORTGAGE180000.0Not VerifiedJul-07Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=92666home_improvementhome improvment loan530xxWI11.930Feb-9511606056839.20%38f0.00.05751.530533603.915000.0751.530.00.00.0Jul-10161.55Jun-07NaN1INDIVIDUAL0NaN0NaNNaN
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39711924029239050005000700.036 months8.70%158.30BB1A. F. Wolfers, Inc.5 yearsMORTGAGE75000.0Not VerifiedJul-07Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=92402credit_cardReduce Credit Card Debt804xxCO15.550May-9401006603323%29f0.00.05698.603286797.805000.0698.600.00.00.0Jul-10159.83Nov-14NaN1INDIVIDUAL0NaN0NaNNaN
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39713906659060785008500875.036 months10.28%275.38CC1Squarewave Solutions, Ltd.3 yearsRENT18000.0Not VerifiedJul-07Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=90665credit_cardRetiring credit card debt274xxNC6.401Dec-86160884726.90%9f0.00.09913.4918221020.518500.01413.490.00.00.0Jul-10281.94Jul-10NaN1INDIVIDUAL0NaN0NaNNaN
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